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Semantic-enabled Applications vs. non-Semantic-enabled Applications

July 17, 2011 Comments off

This entry is offered as background.

Semantic-based applications operate on data. This can be data in a database, entries in a blog or wiki, text in documents or web pages. The data can be real-time data such as clicks in a browser, streaming video, or a stock ticker. The data can be anything. This is of course true of non-semantic-based applications as well.

The difference between semantic-based applications and non-semantic-based applications comes from their use of ontologies.

Semantic-based applications use special information models (semantic models) that describe the data they operate on. These semantic models are commonly known as “ontologies”. Note: ontologies are different than a database schema. And creating ontologies is not the same as “data modeling”.

Ontologies are usually maintained and accessed separately from the data they describe. To accomplish this, semantic-based applications need to have some way to relate the data to the concepts in the ontology/ontologies that describes it. The simplest form of this relationship is via “semantic tags”.

Semantic tags are used to identify data as representing or containing information related to one or more concepts in the ontology. More sophisticated systems employ classification functions that dynamically identify data as representing one or more concepts. Still more sophisticated systems create and maintain mappings of relationships as well as the concepts. And even more sophisticated systems maintain these mappings on multiple dimensions (for example, mapping different “Points of View”).

Semantic-based applications enable reasoning about data by examining the related ontologies. This can be as simple as maintaining a hierarchical index of the semantic contents of a document and then querying the index to find semantically related data. Logic can also be applied to find inferences where, for example, the existence of concept A and B and C in one or more documents implies the existence of concept D. Still more sophisticated systems can determine semantic overlap between the information sought and the information available, or between different “points of View”. And even more sophisticated systems can discover new data and new concepts.

What is important here is this:

  1. Semantic-based applications use an external information model (ontology) to semantically describe data in terms of concepts and their relationships.
  2. There is some way to relate the data to the items in the ontology.
  3. Reasoning can be performed about the data based on its relationship to the ontology.

There are many ways all this can happen. Each has its own pros and cons. This will become evident in later blog entries.

Ontology in the SIRA Technology

July 17, 2011 Comments off

We built our system around the notion that language (for that matter; drawings, songs, smells, or any other encoding of perceived semantics) provides a systematic (although often complex) way to express relationships between and among items. These items may range from the physical to the abstract. The relationships may express time, location, participation, or a wide variety of physical or conceptual relatedness.

When you “teach” SIRA about your interests, SIRA constructs (or adds to) a semantic model (i.e. ontology) representing the concepts and relationships of interest. If these concepts are already known, they may be referenced and perhaps enhanced. If the relationships are already known, they too may be referenced and perhaps enhanced. How this happens will be covered in other blog entries.

A previously existing ontology (perhaps with a supplied upper ontology) may provide a “useful” organization of concepts and relationships. This organization is useful to SIRA in two ways. First, while reading, the specializations and instances of a concept or relationship of interest are automatically recognized. And second, while reporting, the written information can be organized with abstract concepts first, followed by successively more detailed concepts. This is in addition to SIRA’s other report organization schemes.

We should also state that “well designed upper ontologies” can provide an excellent foundation for reasoning that exploits generalization-specialization and part-whole relationships. The topic of reasoning in SIRA will be covered in other blog entries.